In [33]:
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.ensemble import IsolationForest
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import matplotlib.pyplot as plt
i = 1
#canmbiar el valor de csv a agarrar para el entrenamiento
while (i < 71):
# Path to your CSV
path = "D:/UsX/Escritorio/space_apps_2024_seismic_detection/data/lunar/training/data/S12_GradeA/" + str(i) + ".csv"
df = pd.read_csv(path)
df.rename(columns={'time_abs(%Y-%m-%dT%H:%M:%S.%f)': 'date', 'time_rel(sec)': 'time', 'velocity(m/s)': 'velocity'}, inplace=True)
# Interpolate missing data
df.interpolate(method='linear', inplace=True)
# --- Handle outliers (clip velocity values) ---
# Clip values that are outside a reasonable range (modify thresholds based on your data)
df['velocity'] = np.clip(df['velocity'], df['velocity'].quantile(0.01), df['velocity'].quantile(0.99))
# Normalize the data
scaler = MinMaxScaler()
df[['velocity']] = scaler.fit_transform(df[['velocity']])
# --- Smoothing the signal before feature extraction ---
df['smoothed_velocity'] = df['velocity'].rolling(window=50, min_periods=1).mean()
def extract_features(df, window_size=100):
windows = []
for start in range(0, len(df), window_size):
window = df['smoothed_velocity'][start:start+window_size]
if len(window) == window_size:
windows.append([
np.mean(window),
np.std(window),
np.max(window),
np.min(window),
np.median(window)
])
return np.array(windows)
# Extract features using smoothed velocity
features = extract_features(df)
# --- Isolation Forest for anomaly detection ---
iso_forest = IsolationForest(contamination=0.01, random_state=42)
iso_forest.fit(features)
# Predict anomalies (1 = normal, -1 = anomaly)
anomalies_if = iso_forest.predict(features)
# Identify anomaly indices
anomaly_indices_if = np.where(anomalies_if == -1)[0]
# --- Autoencoder for anomaly detection ---
autoencoder = Sequential([
Dense(32, activation='relu', input_shape=(features.shape[1],)),
Dense(16, activation='relu'),
Dense(32, activation='relu'),
Dense(features.shape[1], activation='sigmoid')
])
# Compile and train the autoencoder
autoencoder.compile(optimizer='adam', loss='mse')
autoencoder.fit(features, features, epochs=50, batch_size=32, shuffle=True)
# Reconstruction error
reconstructions = autoencoder.predict(features)
mse = np.mean(np.power(features - reconstructions, 2), axis=1)
# --- Adaptive thresholding based on reconstruction error ---
threshold = np.percentile(mse, 95) # Adjust threshold (95th percentile)
anomalies_ae = mse > threshold
anomaly_indices_ae = np.where(anomalies_ae)[0]
# --- Visualization of anomalies ---
plt.figure(figsize=(10, 6))
plt.plot(df['time'], df['velocity'], label='Seismic Signal', alpha=0.2)
# Mark anomalies from Isolation Forest in red
plt.scatter(df['time'][anomaly_indices_if * 100], df['velocity'][anomaly_indices_if * 100],
color='red', label='Isolation Forest Anomalies', alpha=0.5)
# Mark anomalies from Autoencoder in green
plt.scatter(df['time'][anomaly_indices_ae * 100], df['velocity'][anomaly_indices_ae * 100],
color='blue', label='Autoencoder Anomalies', alpha=0.5)
plt.xlabel('Time')
plt.ylabel('Velocity')
#title ='Anomalies Detected in Seismic Data: ', str(i)
plt.title('Anomalies Detected in Seismic Data: ' + str(i))
plt.legend()
plt.show()
# Plot the smoothed signal for identifying outliers or spikes
plt.figure(figsize=(10, 6))
plt.plot(df['time'], df['smoothed_velocity'])
plt.xlabel('Time')
plt.ylabel('Smoothed Velocity')
plt.title('Smoothed Seismic Signal: ' + str(i))
plt.show()
i += 1
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 955us/step - loss: 0.0317 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.7695e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.3526e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.8795e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9131e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.5831e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.2092e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.5075e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9379e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 8.8691e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.3733e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.0748e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1896e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0786e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0263e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0752e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.1374e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.2829e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 9.5435e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0832e-04 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.2445e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.8456e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 9.6553e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.2671e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.7939e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 7.9480e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 7.1042e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.5162e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 5.2110e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 6.2759e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.3980e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.5900e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.5367e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.4566e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.2979e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 4.7184e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.6136e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.5947e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.8142e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.8130e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.2847e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.5675e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.2559e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.0534e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7190e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.0880e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 1.7276e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 2.5583e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.1476e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 1.7267e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 938us/step - loss: 0.0174 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.6279e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 4.2754e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 2.5839e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.1537e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.6389e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.0771e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 2.0860e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.8399e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.8185e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 1.5185e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3606e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4439e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6140e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2067e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0730e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0108e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 837us/step - loss: 1.3080e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 826us/step - loss: 9.6830e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 837us/step - loss: 8.0137e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.8792e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 4.4042e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 5.1724e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 3.8520e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.9409e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 826us/step - loss: 4.6317e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.8968e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 848us/step - loss: 3.6117e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 4.3093e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 826us/step - loss: 3.9186e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 3.5156e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.0373e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 820us/step - loss: 3.3426e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 815us/step - loss: 3.2873e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.5416e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.6700e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.9856e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 2.8812e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.6373e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 3.4183e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5988e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 3.0467e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.8169e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1376e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 3.1381e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5332e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.3726e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.7345e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5661e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.7119e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0272 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.3948e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 2.4695e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.1194e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 891us/step - loss: 2.9978e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 851us/step - loss: 2.4836e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 958us/step - loss: 2.6180e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.3169e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.3848e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.9007e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.0303e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 1.0490e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.0610e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 9.1600e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 815us/step - loss: 1.8641e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.5907e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 837us/step - loss: 8.3981e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.4679e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.1364e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.0362e-04 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2187e-04 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.0580e-04 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.0161e-04 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 7.2838e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 1.0064e-04 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.0741e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 9.3533e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 1.0778e-04 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.1393e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.7427e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 815us/step - loss: 8.9872e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 848us/step - loss: 4.1116e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 837us/step - loss: 4.5490e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 826us/step - loss: 6.0573e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 832us/step - loss: 4.8912e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 6.9024e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 3.9623e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.2126e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 4.8459e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 3.2436e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 3.3165e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.8685e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.7865e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.2464e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.8149e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0049e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1432e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6751e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.7696e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.0299e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 972us/step - loss: 0.0264 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.4843e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.9431e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.0317e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.1834e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.0099e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1411e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.6873e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0307e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7460e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.1935e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7488e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.1928e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0587e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.0263e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.0673e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.2434e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3778e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 1.2799e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.5868e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.3338e-04 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.3306e-04 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.1809e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.2220e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 7.0043e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 6.7675e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.7270e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.5168e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.0879e-04 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 8.1205e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 7.3195e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 1.4110e-04 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.0009e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 5.1669e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 8.1526e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.9875e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 7.1205e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 4.9559e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 5.3646e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.9538e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 3.9978e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.4204e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.2657e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.7548e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.1256e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 2.6289e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 2.0125e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7500e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 2.1031e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.5857e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 927us/step - loss: 0.0275 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.2130e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.8084e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2876e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9271e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9010e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.8398e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.2235e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1291e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9110e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 7.8092e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.3613e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4806e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.1053e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 8.6999e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2907e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.9213e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 5.6759e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.4158e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 5.3902e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.9797e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.5473e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 837us/step - loss: 2.4408e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.4827e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6625e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.0366e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.5654e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.2902e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.7095e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2622e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.1209e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.7707e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1875e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.2239e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.8079e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9867e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.5413e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6343e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1514e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.7260e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2617e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.3480e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0831e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.2515e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.2295e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.3169e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.0897e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.0260e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3712e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 8.6336e-06 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0205 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.6618e-05 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.2800e-05 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 7.0171e-05 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 7.9733e-05 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.3898e-05 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 3.4297e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.7224e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.0981e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.6492e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.0735e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1071e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.8020e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.9838e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.2869e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 2.1878e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.3818e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.1482e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.1682e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.8281e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 3.2445e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.9753e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.3892e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.8456e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.3332e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.8801e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.5911e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0936e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2956e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.0697e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.4218e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.3598e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.2133e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4471e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 2.0418e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.3198e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.0970e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.9989e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.5584e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7576e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.4873e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7409e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8613e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.3388e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.8120e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.5906e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.9060e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.6633e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.8412e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 1.3695e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 938us/step - loss: 0.0330 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3298e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.7407e-05 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0560e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.5234e-05 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 5.1001e-05 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 5.9027e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.7406e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.3307e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 848us/step - loss: 3.0932e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.4932e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.0070e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 3.2557e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.8593e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.2724e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.3718e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.1726e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.3616e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 4.0930e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.9823e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.6288e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.2832e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.7220e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.6712e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 3.4350e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.8470e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.3836e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.8516e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.2688e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 3.4431e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.5491e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.3393e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5877e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.7482e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.6930e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1366e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.5192e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.5724e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9705e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.1987e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.9591e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.8142e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.3995e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.9351e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.5079e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.3151e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.3640e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.2189e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4237e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 2.5453e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0195 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.8700e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.9884e-05 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.2217e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.5597e-05 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 6.5202e-05 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.0818e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.8033e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.4629e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.1037e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.1133e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.0266e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.4599e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.7614e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.1579e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.5801e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.3859e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.4077e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.3890e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.7888e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.2123e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.6156e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.3121e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.3752e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.9130e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.1513e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1300e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.4681e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.8004e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.4375e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 2.7148e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.5780e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.6372e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.8638e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 2.6480e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7761e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.5715e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.3418e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.0568e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6918e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.8688e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7773e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.1469e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.8251e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0785e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.7290e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8621e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.7545e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7044e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4175e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 944us/step - loss: 0.0309 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.9107e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.4686e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 2.0993e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.7066e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.3025e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0724e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.0627e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 837us/step - loss: 1.3625e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 1.0046e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1507e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 9.5954e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.1977e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.9278e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.7133e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0510e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 8.0750e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 6.9999e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.8717e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 7.6041e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.4850e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 7.5208e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 8.3078e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.2973e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.7066e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 6.5518e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 7.0794e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.6171e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.2877e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.3837e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 5.0887e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.6735e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.9341e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.0954e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.1435e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.8274e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.7075e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 3.3289e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.7220e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.7920e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.9866e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.9681e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 2.5194e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.9634e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.3550e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.6648e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.5569e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.7513e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4925e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2165e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 961us/step - loss: 0.0320 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 6.8494e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.3270e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.7517e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.0653e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.1138e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.3682e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.8261e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.0132e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.1255e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.7500e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.3562e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.4559e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.6212e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.0855e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6716e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 1.6382e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.3332e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.1601e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.0364e-04 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.0270e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 5.0901e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.1653e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.7861e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.7130e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.3248e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.2724e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.8266e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.3043e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.4990e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.6692e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.3179e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.5689e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.5926e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.0587e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.2351e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.9015e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.7620e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.0239e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 3.2635e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.5907e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.5299e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.9526e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.7499e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.8101e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.2101e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.8504e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.6151e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.8911e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.2327e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 950us/step - loss: 0.0260 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.4730e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.5686e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1737e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.5906e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.3923e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.4398e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.1956e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.8237e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6602e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.3995e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.4178e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1137e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2542e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.2401e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.3006e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.1827e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.5374e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.0371e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.3551e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 8.7214e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 6.5466e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 6.8068e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.9338e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.9433e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.1000e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.4002e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.2590e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.9804e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.5650e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.9666e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.4182e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 5.5322e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.3102e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.7435e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.0049e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 4.1045e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.7716e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.3645e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.4324e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.0159e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 3.7089e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 3.3683e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 3.3463e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.6986e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.9261e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 3.1237e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.1125e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.6101e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.8871e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 972us/step - loss: 0.0221 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 9.1434e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 8.5452e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.0672e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.6590e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.1150e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.1139e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4417e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.6111e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.0985e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.0926e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.0792e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.5515e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.2572e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0833e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 9.3198e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 7.6288e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 7.1789e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 6.6146e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 5.4585e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.9342e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.0214e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.1241e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.4562e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.7080e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.6608e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.0591e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.0383e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.3578e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.1922e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.3792e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.3942e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.0463e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.8989e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.8778e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.1864e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.7649e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.5717e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.7721e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.5137e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.5898e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.4793e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.3904e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1831e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.9663e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.0830e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.4743e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.8105e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.8626e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 1.9572e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 955us/step - loss: 0.0254 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.6388e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.9624e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.4555e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 2.2172e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.0390e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.5060e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1964e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.2385e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.3147e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 9.2013e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.2827e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.0105e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.9823e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.0415e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1462e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.5557e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.3645e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 7.3596e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.1245e-04 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.1897e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.3705e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.3661e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.2503e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.6738e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 4.1502e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.7702e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.0907e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.1023e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.8062e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.7214e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.2428e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.6078e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.4531e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6848e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7967e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.6983e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.8169e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.4116e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.2227e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0770e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8142e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2297e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.5871e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.4414e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6120e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.1551e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.2685e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.3005e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1534e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 972us/step - loss: 0.0179 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.2604e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.7230e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.3855e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.1513e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.4861e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.7229e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.3720e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9349e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.4091e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.2168e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.5031e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.4199e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.5866e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.8907e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.7224e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.2495e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0062e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.2934e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 9.8105e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.0838e-04 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.2473e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.8840e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.3651e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.1384e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.0048e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.3963e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.9052e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.8780e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 4.9289e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.4163e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.0259e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.8960e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.0407e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.1094e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.8796e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.5195e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.7434e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.1196e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.6614e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.3162e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.7257e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.6289e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.9743e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.2346e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.5919e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1986e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4718e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5661e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.5304e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 989us/step - loss: 0.0176 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 6.5877e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.7551e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.8722e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0561e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.6822e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9968e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8349e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.8722e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.3007e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2440e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.3912e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.9646e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.2848e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.1866e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.6391e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.5822e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 4.5487e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.8150e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.0459e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.9631e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.4105e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.7628e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.8336e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5726e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.0039e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.8443e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6554e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.3505e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.8361e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.0766e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.3592e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.2032e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5733e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.8923e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.6387e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.4899e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.8728e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 2.1660e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.7700e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.5721e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.5644e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.6617e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.3993e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2719e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6207e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.4819e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2948e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.8272e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.8941e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 961us/step - loss: 0.0147 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.5586e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.7051e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 1.7727e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.2102e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.3988e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.4728e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5867e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.3253e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 9.8206e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.3870e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.3284e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 7.2363e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 8.6516e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 6.7425e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.9775e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 9.5658e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.3420e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.3099e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.7394e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.2205e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.3259e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.5436e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.1189e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.5419e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.0788e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.3920e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 6.8831e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 7.8061e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.3917e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.9668e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.0697e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.1094e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.0634e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 848us/step - loss: 3.9256e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.5164e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.3729e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.9684e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.9813e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.5722e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9011e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.9350e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6524e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7349e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6080e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.3463e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.5242e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.4398e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.2512e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4291e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 961us/step - loss: 0.0154 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.8578e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.9534e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.5962e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.2813e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.8378e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.3569e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7859e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.8058e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7998e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.3891e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.1250e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8931e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.5790e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5335e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1882e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.2799e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.2024e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.5129e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.0039e-04 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.7785e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.9722e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.8300e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 7.2240e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.1999e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 4.8123e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.0296e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.8559e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.9318e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.4096e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.6661e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.6183e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.3429e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.6366e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.5851e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.2452e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.6892e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.3236e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.6424e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.1875e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.3662e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.1924e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.7003e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.4562e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3842e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7760e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2142e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.7254e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.5763e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4402e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 972us/step - loss: 0.0191 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 5.5185e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.1665e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.3560e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.2489e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.7022e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.4212e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.1615e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5261e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1579e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.8036e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.5394e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.3138e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9972e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6866e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.7039e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.4623e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.4789e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0365e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4583e-04 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1551e-04 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.2827e-04 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 9.9325e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.8753e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.5963e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 5.8829e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.0038e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.0041e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 7.2282e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 5.8940e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.8347e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.4582e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.4064e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.8068e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.2352e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.5355e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.9884e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.3222e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.9798e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.9495e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.2311e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.1351e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.8601e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.7454e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6148e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.4243e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3298e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6581e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.5679e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.4421e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0236 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 4.6242e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.0260e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.5685e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0642e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4772e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.4254e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.4479e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.6943e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.0378e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.0284e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.0246e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.1307e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.7421e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.9959e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.0308e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 6.8560e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 6.2958e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.3774e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.0408e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.5942e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.2187e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.4550e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.8181e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 3.0724e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.8203e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.0018e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.9791e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6233e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5599e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7226e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.4042e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.0798e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.8251e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.7598e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4380e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1332e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.3061e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.3058e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.1274e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1683e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1404e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.3919e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.2460e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.1133e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.0837e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.0590e-06 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.3214e-06 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1993e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.1417e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
149/149 ━━━━━━━━━━━━━━━━━━━━ 1s 987us/step - loss: 0.0250 Epoch 2/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 892us/step - loss: 2.3348e-04 Epoch 3/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 1.5019e-04 Epoch 4/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 892us/step - loss: 1.3378e-04 Epoch 5/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 1.5307e-04 Epoch 6/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 872us/step - loss: 1.2857e-04 Epoch 7/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 912us/step - loss: 1.2951e-04 Epoch 8/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 879us/step - loss: 1.2940e-04 Epoch 9/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 1.2631e-04 Epoch 10/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 872us/step - loss: 1.1909e-04 Epoch 11/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1730e-04 Epoch 12/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.5844e-05 Epoch 13/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 5.7138e-05 Epoch 14/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 892us/step - loss: 4.2106e-05 Epoch 15/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 879us/step - loss: 3.4716e-05 Epoch 16/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 4.1044e-05 Epoch 17/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 885us/step - loss: 4.1963e-05 Epoch 18/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 885us/step - loss: 3.4284e-05 Epoch 19/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 885us/step - loss: 4.0263e-05 Epoch 20/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 4.5869e-05 Epoch 21/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 919us/step - loss: 4.0146e-05 Epoch 22/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 912us/step - loss: 3.0149e-05 Epoch 23/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 912us/step - loss: 4.3363e-05 Epoch 24/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 885us/step - loss: 3.6269e-05 Epoch 25/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 885us/step - loss: 3.3850e-05 Epoch 26/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 885us/step - loss: 3.6735e-05 Epoch 27/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 892us/step - loss: 4.0080e-05 Epoch 28/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 879us/step - loss: 3.9204e-05 Epoch 29/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 946us/step - loss: 3.3347e-05 Epoch 30/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.8707e-05 Epoch 31/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.6659e-05 Epoch 32/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 3.8965e-05 Epoch 33/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 4.0490e-05 Epoch 34/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 892us/step - loss: 3.5497e-05 Epoch 35/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 872us/step - loss: 2.8794e-05 Epoch 36/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 919us/step - loss: 3.5557e-05 Epoch 37/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 879us/step - loss: 3.5058e-05 Epoch 38/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 872us/step - loss: 3.0534e-05 Epoch 39/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 912us/step - loss: 2.4486e-05 Epoch 40/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 872us/step - loss: 2.6819e-05 Epoch 41/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 879us/step - loss: 2.7070e-05 Epoch 42/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 892us/step - loss: 2.4053e-05 Epoch 43/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 2.7397e-05 Epoch 44/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 3.0396e-05 Epoch 45/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 2.2865e-05 Epoch 46/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 946us/step - loss: 2.5266e-05 Epoch 47/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 879us/step - loss: 2.9977e-05 Epoch 48/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.9783e-05 Epoch 49/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 885us/step - loss: 2.4560e-05 Epoch 50/50 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 872us/step - loss: 3.4074e-05 149/149 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 972us/step - loss: 0.0186 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.2392e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 3.1838e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9102e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5986e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.8729e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 1.2748e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.1445e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.1349e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 9.1539e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 6.3040e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.2238e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 5.1784e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.2999e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.9436e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.1832e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.7812e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.0502e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.2563e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.2211e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.3565e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.2998e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.4670e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.2066e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 837us/step - loss: 1.9530e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.8858e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.7766e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0898e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.8947e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.7513e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.6903e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.5411e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.6012e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.2230e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.4324e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.4414e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.4477e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.4628e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2637e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2268e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3986e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3184e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1564e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1827e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1851e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2616e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1562e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.0647e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1669e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.4552e-06 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0323 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 3.4937e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1520e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.6571e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3456e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.0701e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.2559e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 7.6897e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 7.3645e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 7.2874e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.8763e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.0228e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.3335e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 7.4115e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.6903e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.5127e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.4451e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.4107e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.0605e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.9414e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.3472e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 8.9282e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.9108e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 6.3613e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 5.9634e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 8.5530e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.0053e-04 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.6769e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 5.4798e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 6.4158e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 6.3058e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 4.9966e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.3064e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.9050e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.7637e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.7250e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.1394e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.8818e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.3563e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.7953e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.0125e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.7100e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.1670e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.5960e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 3.8176e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.1487e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.5720e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.6712e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.0670e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.9940e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 983us/step - loss: 0.0218 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.5924e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.1742e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.4963e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6303e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1261e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.8583e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.7157e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 7.7524e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.8538e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.2103e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.1236e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.4349e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 5.5653e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.6763e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 6.7258e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 8.3386e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.6055e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.1714e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.9623e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.1499e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.4362e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.3143e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.5421e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 8.0902e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.4524e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.9803e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 7.1288e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.6874e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.0215e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.2619e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 5.8767e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.1258e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.7445e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.5461e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.1837e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.8379e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.2106e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.6047e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.3356e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.5194e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.4456e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.6096e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.0026e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.3332e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.4929e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.7334e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.1905e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.9666e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6562e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 989us/step - loss: 0.0223 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.4132e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.0341e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.0416e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.5199e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.2767e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.1798e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.3270e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 7.2275e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.0467e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.4540e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.4332e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.1061e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.0047e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.3807e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.5202e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 6.0580e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 6.9254e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.7686e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.8274e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.1482e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.7424e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.9705e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.8902e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.5117e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.6571e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.2384e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.4183e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.0997e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.7362e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 3.9827e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.2626e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.0333e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.8872e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.8587e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.4937e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6346e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.7812e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.0970e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4919e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.0050e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.1399e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.0132e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6019e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9295e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0152e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.4770e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.6052e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4896e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6235e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0245 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.5559e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 9.5392e-05 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 9.8817e-05 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.1390e-05 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.4444e-05 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.3190e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.4441e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.8895e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.8259e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.2936e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2519e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.0554e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.2839e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 848us/step - loss: 1.6530e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.7786e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7008e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.8874e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.8337e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.8228e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.6234e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.7549e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0559e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.1660e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.3469e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.8205e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 2.0365e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7140e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6339e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.8293e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9470e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.7272e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.5980e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.8943e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 1.7876e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.6181e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 2.1525e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2176e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.7090e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0268e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.7802e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7755e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 1.5433e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.6595e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.8025e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.9166e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5377e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0479e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.5888e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6021e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0283 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.5470e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.2058e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 9.9512e-05 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.0269e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 9.2754e-05 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.7418e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.7432e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 8.2333e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 7.7640e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.1025e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.8916e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.7909e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.2051e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.9221e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.2066e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0145e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.8266e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6894e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.7697e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.9914e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.1797e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.6190e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1180e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.8469e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.7605e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.6633e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.8610e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.7698e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.5431e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.3847e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9437e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.6560e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.5631e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.5381e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.6346e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.3296e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.4346e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.4498e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.4163e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 1.3082e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5202e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4715e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.3249e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.5126e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.3427e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1229e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.1129e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.1500e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.4312e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0366 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 4.7285e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.7829e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0234e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0619e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.1324e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2752e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.3857e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.0397e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.2147e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.7015e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.0640e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.1835e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.5180e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.7817e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.2804e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.6841e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.9060e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.9224e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 9.9301e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 5.5438e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.7188e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 5.7300e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.6831e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 5.4979e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 6.0964e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.1519e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.1211e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.9532e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 4.8757e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.5614e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.0119e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.0623e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.2596e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.2518e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.8884e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.0710e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.7545e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.2799e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.4335e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.4944e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.8738e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.6830e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.6517e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.1011e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 2.5522e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9338e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.9771e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.8066e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9513e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0306 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.3354e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.7551e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4661e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2595e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.2014e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.5665e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.1611e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.7943e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 9.0651e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.7852e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.3114e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4280e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0413e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.2535e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.1278e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0541e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 1.0425e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1082e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.0994e-04 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.9581e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.6147e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.1840e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.4341e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 6.3110e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.1397e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.3464e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.9744e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.7936e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.1897e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.9455e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.1726e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3182e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9512e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.8036e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0445e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1769e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7888e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6531e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.4153e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.8189e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.6421e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.3326e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.2874e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.4470e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.1530e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7809e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.3501e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 1.2136e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.6035e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0211 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.3294e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.2503e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.0999e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.7540e-05 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 8.9785e-05 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.1684e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.7109e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.8557e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.4276e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.0659e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.8500e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.9606e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.4922e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 6.0615e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.5284e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.7857e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.0662e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.2240e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.1630e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.8223e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.8278e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.6658e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.5675e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.4927e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.0790e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2751e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.3218e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.5453e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6766e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.3745e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7067e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0924e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.7639e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.4210e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6024e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9151e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.8543e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.7688e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1525e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.1514e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.2794e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.2433e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0077e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.9652e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9065e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.0549e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.1022e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.6201e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.8881e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 3s 1ms/step - loss: 0.0263 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.4835e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.9557e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 1.9893e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.3962e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 1.2087e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.4452e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.8127e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4534e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.3292e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.7329e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0797e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.3829e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.0501e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1030e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.9689e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 9.4591e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.8709e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0736e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.1088e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 5.8212e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 5.4630e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.7866e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.3791e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.9636e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.6721e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.3190e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0160e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7824e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.3641e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.4154e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.8182e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.8882e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0000e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.7753e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.8847e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.5586e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 2.1290e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.8010e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.4815e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.4444e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6211e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.5394e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.6391e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.1431e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.5270e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.7663e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.2588e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.2258e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.5856e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0278 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.2587e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2701e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.1370e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6598e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.2771e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.0923e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 6.3057e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.1742e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.1493e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.3389e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 8.9237e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.5778e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0792e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 6.3461e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.9818e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.1859e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.3443e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 5.6860e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.9421e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.2429e-04 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.8299e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.8233e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.2849e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 5.4110e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.1941e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.9245e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.0785e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.2267e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.5663e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.4600e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.3706e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.7185e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6607e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.2364e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.0482e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.9319e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.5835e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.2623e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7013e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.4124e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.5175e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.3689e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.2048e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.2742e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.7299e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.1241e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.0618e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1721e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.0755e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0225 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.8944e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.9705e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.5948e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.9575e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.4312e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.0279e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2518e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6971e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1493e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.7156e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2396e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.7863e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.5228e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.0283e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 8.7546e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.5059e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.6399e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.4653e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1973e-04 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.1216e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 7.8789e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.9624e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.6221e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.9899e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.7311e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.7612e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.6637e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.2106e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.7265e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.9482e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.7092e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.8538e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0708e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.7398e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.9067e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.8083e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.4011e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2144e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.9026e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.0659e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.4441e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.0787e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0121e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.6422e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6488e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4676e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.4658e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 1.3054e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.1540e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0280 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 0.0011 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.8632e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.8124e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 6.3912e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 5.6353e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.1424e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.9037e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.9534e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.4477e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.4489e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.7469e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.5126e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.5584e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.3402e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.2200e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 9.3565e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.2516e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.9577e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.3131e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.8422e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 6.9431e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.5442e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.6848e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 6.0829e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.9114e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.7380e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.6928e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.9811e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.3834e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.2053e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.0324e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.8021e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.3363e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.0514e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.5681e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.0514e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.1870e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.5112e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.3052e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.4620e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.1750e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.8735e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.2551e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.1798e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.3296e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.4916e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.7133e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.3247e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.7636e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0227 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.8021e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.2359e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 8.0754e-05 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.2955e-05 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.3456e-05 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.2994e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 6.0228e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.7215e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.6142e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.3244e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.9111e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.2030e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 6.7543e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.4732e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 6.1371e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.8012e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.5401e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.7025e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.1530e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.3809e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.1793e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.5219e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.4004e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.9973e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.4587e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.8384e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5762e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.1328e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.5599e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7974e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2546e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.5126e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.2390e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.1027e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1668e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4012e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.1219e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.2224e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.6291e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.8339e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.3033e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.4320e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.1766e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.4477e-06 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0011e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.6486e-06 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.0008e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 9.3462e-06 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.8814e-06 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0312 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 0.0013 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 0.0011 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.5858e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.2523e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 6.6906e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.7402e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.4168e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.7082e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.8359e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0922e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.7522e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.4212e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.3010e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.1311e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.2656e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0819e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.1400e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 8.9808e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 9.8749e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 8.6748e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 8.5512e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 8.2498e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.1410e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.2552e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 8.3256e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.6932e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 6.3630e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.8359e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.4788e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 5.5283e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.4788e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.9816e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.7942e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.0202e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.8865e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.3790e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.1094e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.4518e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.6890e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 843us/step - loss: 1.6709e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.8070e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.6320e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6189e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.5813e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.3558e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.4597e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1464e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.3109e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3452e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0226 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.2944e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.9545e-05 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 8.4838e-05 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 9.0739e-05 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 8.9294e-05 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.9317e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 7.6128e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.6237e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.1029e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 1.9771e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0716e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.9862e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.2034e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4377e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.2232e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.9197e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9410e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.1932e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.1291e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.5750e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.8182e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.0953e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.8850e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1303e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0199e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.1410e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1636e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0976e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.7663e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.0522e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.3433e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.1509e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.0351e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2582e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.9256e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.7768e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.5977e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.8855e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7992e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.9053e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9256e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.5885e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.9478e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.8479e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.8440e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 1.9612e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0930e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.9480e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0177e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0382 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.7121e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.7973e-05 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 9.2344e-05 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 9.4408e-05 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.3373e-05 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.2474e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 3.3960e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.2329e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.2842e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 2.4705e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.4400e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.1584e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.9682e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0310e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.0927e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.9250e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.4456e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9919e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.9892e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0007e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.2131e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.2817e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.3800e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.1695e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1214e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.9555e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9909e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 1.7257e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.3216e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.0126e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9225e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.3476e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.8702e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9769e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.7716e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.0470e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.2101e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0210e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.1513e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0853e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2507e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0681e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.3045e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9928e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.2138e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.0797e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9786e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9371e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.2291e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0165 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.2848e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6415e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.6451e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.7926e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.2049e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.7811e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 8.5922e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.1369e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.5696e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.2582e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.3538e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 7.2518e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.3390e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.2580e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.6178e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.6270e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.0138e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.7742e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.6770e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 8.1892e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.2831e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.4921e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.3405e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.9607e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.2243e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.6478e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.4250e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 6.1100e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.9719e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.3917e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.2844e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.6635e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.9707e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.6196e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.9931e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.8713e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.7957e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.2661e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 3.7266e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.6981e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.0589e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.4960e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.1618e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.4360e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.3802e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.1058e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.7181e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6122e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.1283e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0245 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.3769e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.9327e-05 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 7.1735e-05 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.0672e-05 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.5371e-05 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.4924e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.3574e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.6145e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.2050e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.4986e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 2.2363e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.3010e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.3358e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.2030e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9055e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.2183e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.9090e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9869e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.4911e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.1977e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.8858e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9006e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.1522e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9518e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2966e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.9241e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.0695e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.9346e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9086e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.7045e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.0347e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.1522e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.8720e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.7239e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2286e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.7932e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.8516e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.8322e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9889e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0839e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.5361e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.0388e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.0822e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0724e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 2.1531e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.7064e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.2595e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.7754e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.1254e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 989us/step - loss: 0.0204 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.8889e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 2.4410e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9184e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2159e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 1.6026e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.7263e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.2821e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 7.6513e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 8.7260e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.8048e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 5.2635e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.7164e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.9532e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.6835e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.0552e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.4800e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.5919e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.3434e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 8.1168e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.5069e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.8665e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.2201e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.9182e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.6600e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.7996e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.9774e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0882e-04 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.7787e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.4520e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.2676e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.4123e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.4119e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.3432e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.5973e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.0453e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.4146e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 3.0936e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.9381e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.0758e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.7782e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.9633e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.6815e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.6707e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.1998e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5362e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 2.3413e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.4519e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.5550e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2225e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 983us/step - loss: 0.0199 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.8507e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.0097e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.2130e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.2280e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.9716e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.9301e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8597e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.5747e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2649e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7976e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 1.1071e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7733e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.3185e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0491e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.3928e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.1216e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.2211e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.4961e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0276e-04 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 9.1826e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.9219e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.8748e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.9997e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.3514e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.7355e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.4564e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.9511e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.3788e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5536e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.5316e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.1060e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.2400e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.8263e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.2590e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.5542e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.9124e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9321e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.3744e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.0659e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.6065e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.6847e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0377e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.5408e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.6551e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.7412e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.5493e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.5369e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1479e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9728e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 989us/step - loss: 0.0265 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4727e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.9901e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8576e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.7728e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4954e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.2975e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0164e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.8122e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.3031e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 9.7079e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.8969e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 5.2402e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.7562e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.4935e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.0299e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.3165e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.9480e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.4182e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.8887e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 6.4444e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.2510e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.8518e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.2283e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.9920e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.6211e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.5658e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.6266e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.9297e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.5921e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 5.9005e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.3273e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.1543e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.4966e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.5995e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.2676e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.1212e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.8013e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.9643e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 3.0857e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.0042e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.1654e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.9419e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.8833e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.3124e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.9732e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.7763e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.8983e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2997e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.1429e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 983us/step - loss: 0.0280 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.4785e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 6.3635e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 5.0312e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.7354e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.0447e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.0092e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.2880e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7515e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.1322e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.0106e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.0895e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.0867e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.7600e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.8156e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 6.3229e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.3124e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.1920e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.9232e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.1588e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.9277e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 5.0595e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.9661e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.2194e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.0327e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.9332e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.1036e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.3612e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.7383e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.3255e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.2547e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.6099e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.9388e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.1305e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 2.7767e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.5769e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.5025e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5780e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 2.6609e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 2.8639e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.0942e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.2763e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.2966e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.3077e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.2061e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.1022e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3754e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3992e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1191e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.7558e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0151 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.8060e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.0116e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 1.8551e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 9.4590e-05 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 9.8817e-05 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.9032e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 8.6553e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.2819e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.4422e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.8723e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.8669e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.9869e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 5.4171e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 4.1714e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 8.7623e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.9449e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.0357e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.2881e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.9428e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.5157e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.1416e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 3.8463e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.0774e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.4523e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.0313e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.6683e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.5803e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 3.5123e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.6130e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.4703e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.9445e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.7670e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 2.9883e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 2.1684e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1355e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0545e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2702e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6247e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.6513e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9065e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7172e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4577e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3693e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2564e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0503e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.1742e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.0608e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.7857e-06 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.0396e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
107/107 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0254 Epoch 2/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 3.8738e-04 Epoch 3/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4266e-04 Epoch 4/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.0363e-04 Epoch 5/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 878us/step - loss: 7.0324e-05 Epoch 6/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.4512e-05 Epoch 7/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 5.7061e-05 Epoch 8/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 962us/step - loss: 4.6700e-05 Epoch 9/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 3.6722e-05 Epoch 10/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 953us/step - loss: 3.1920e-05 Epoch 11/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 925us/step - loss: 2.1208e-05 Epoch 12/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0560e-05 Epoch 13/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7949e-05 Epoch 14/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2555e-05 Epoch 15/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6880e-05 Epoch 16/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9868e-05 Epoch 17/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6920e-05 Epoch 18/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 887us/step - loss: 1.5564e-05 Epoch 19/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 934us/step - loss: 1.6601e-05 Epoch 20/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.8720e-05 Epoch 21/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.8872e-05 Epoch 22/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6582e-05 Epoch 23/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.6029e-05 Epoch 24/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.8857e-05 Epoch 25/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.7160e-05 Epoch 26/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6979e-05 Epoch 27/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3858e-05 Epoch 28/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 887us/step - loss: 1.8375e-05 Epoch 29/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6649e-05 Epoch 30/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.5190e-05 Epoch 31/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 953us/step - loss: 1.5923e-05 Epoch 32/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7097e-05 Epoch 33/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 1.8414e-05 Epoch 34/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7822e-05 Epoch 35/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.8474e-05 Epoch 36/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1493e-05 Epoch 37/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 934us/step - loss: 1.7012e-05 Epoch 38/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7069e-05 Epoch 39/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.9703e-05 Epoch 40/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 896us/step - loss: 1.8170e-05 Epoch 41/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 915us/step - loss: 1.4163e-05 Epoch 42/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 925us/step - loss: 1.6200e-05 Epoch 43/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 934us/step - loss: 1.5422e-05 Epoch 44/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 925us/step - loss: 1.9632e-05 Epoch 45/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 934us/step - loss: 1.8334e-05 Epoch 46/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 925us/step - loss: 1.6916e-05 Epoch 47/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 953us/step - loss: 2.1329e-05 Epoch 48/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 878us/step - loss: 1.4985e-05 Epoch 49/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 915us/step - loss: 1.8072e-05 Epoch 50/50 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - loss: 1.4175e-05 107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 989us/step - loss: 0.0246 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.6005e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 4.5260e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.9643e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.5189e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.4486e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.5862e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.0972e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.2574e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.4472e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.9863e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.5821e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 8.1173e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.5734e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 6.9728e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 7.7945e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.4703e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 6.4065e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.3276e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.4593e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.0466e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.7053e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 5.5154e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.0293e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 5.3144e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.6124e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 5.9689e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.2615e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.0837e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 6.1803e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.5702e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.9297e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.3753e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 5.9200e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.8483e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 5.1955e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.6212e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.3538e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.2835e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.1141e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.3599e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.2819e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1606e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 2.3838e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0133e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.4531e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8762e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.1306e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9847e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4097e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 983us/step - loss: 0.0347 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.2381e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.1170e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 979us/step - loss: 3.1827e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1718e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9874e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.4708e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.1340e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.5905e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.7563e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.2834e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0728e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.0228e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.0611e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.5261e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0550e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 8.7060e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.0367e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.0629e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.3823e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.5892e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 8.4620e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 8.2957e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.3308e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 6.1969e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.0042e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.8632e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 4.7129e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.8327e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.0144e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.1955e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.3078e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.5237e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.7631e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.8934e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.5294e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.0295e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.3080e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.7728e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3440e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.9730e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9875e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1961e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.8884e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.3341e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.1911e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.7040e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7268e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.2235e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9031e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 967us/step - loss: 0.0328 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 3.8097e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.9861e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.9249e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.4182e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6250e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.3440e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 8.8288e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1585e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.6121e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.9258e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.4788e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.0989e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.3436e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.1529e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.7771e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.1396e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.1750e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.3926e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 7.9920e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 7.8752e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.9409e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 6.7321e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.8662e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.8980e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.0262e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.7213e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.5434e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 3.7148e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.1227e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.4832e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.3132e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.0330e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.2975e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.8077e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8167e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.9397e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.4346e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.2596e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9242e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.0996e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.7246e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.5450e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6437e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.4185e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9947e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4203e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4010e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.5323e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6028e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 3s 1ms/step - loss: 0.0274 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.7248e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.9171e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0012e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 8.1514e-05 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.1650e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.0053e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 5.8724e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.4412e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.8530e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 8.1667e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 6.8955e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.0656e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.0594e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 7.6092e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 5.7938e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.3898e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 6.0513e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.6496e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.0849e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.3076e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6709e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0563e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.6989e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.9707e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.3537e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.0807e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.4164e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7090e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2570e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.5105e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.4114e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 2.0212e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0874e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4545e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.3769e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.3327e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.2327e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 9.0448e-06 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0404e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0105e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0063e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 8.5614e-06 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0441e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.0737e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.5019e-06 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.3041e-06 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 8.7665e-06 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2775e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.4674e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 995us/step - loss: 0.0303 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 0.0011 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 6.5484e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.9910e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.1926e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.0439e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 3.0104e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.3084e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.6299e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5848e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.3810e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.4862e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.0988e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.1559e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0487e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.0635e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0584e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 8.7944e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.1741e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.5104e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.9655e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.0987e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 6.3391e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 6.1311e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.3106e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 6.7621e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.9197e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.6774e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.8638e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.4333e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.0020e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.1304e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.8681e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.0847e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.2648e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 4.0977e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.3950e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.6081e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.2339e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.7668e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.9685e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.3514e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 3.9763e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.4385e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.1649e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 2.9770e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.8407e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.0633e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.7072e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.5401e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 983us/step - loss: 0.0270 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 4.9334e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.7327e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.5585e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.1765e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.2403e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.0214e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.7574e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4900e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.6019e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.4837e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 9.3669e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 8.0648e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0616e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.0209e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 8.2999e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.5776e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.7012e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.1796e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.7871e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 4.5625e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.5602e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.0694e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.3632e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.1713e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.6044e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.0905e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.6640e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 2.3633e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.5990e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 2.7382e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.5146e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3951e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.5202e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.9656e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0776e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2928e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.4672e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.8744e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9329e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.1997e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9611e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7984e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6880e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.9051e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.5861e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.8442e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4967e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.8118e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.7710e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 995us/step - loss: 0.0362 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.2320e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.4079e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.0355e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.8606e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.5583e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.3859e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1634e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4501e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.2673e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.0770e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4197e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.1273e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.6298e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.4662e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2859e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.1694e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 1.1621e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.3652e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.6759e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.1448e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.7196e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.7733e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.4655e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.0726e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 3.8767e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.7389e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.0183e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.0299e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.4198e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.4097e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.4165e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.4749e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.5158e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.1226e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.1256e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 2.2878e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.1506e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4947e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.3102e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.4027e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7911e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.7597e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.3430e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.7827e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7091e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.5999e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.8646e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7871e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1323e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0166 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.8697e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.2161e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.5309e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.1882e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 9.4777e-05 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.1348e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.2097e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.2474e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.7533e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.3561e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 7.4188e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.6297e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.5512e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0132e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.6456e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.9370e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 6.9406e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.0491e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.5878e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 4.7127e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 5.2048e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.7289e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.4220e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 5.9979e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.0294e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.1018e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.1939e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.6976e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.7699e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 5.2216e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.1718e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.4849e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.8393e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.1170e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.0662e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.7020e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.1562e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.2168e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.5635e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.8700e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0124e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.0450e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2308e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.1932e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.8668e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.7121e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4098e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6775e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.2392e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0259 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.1125e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 3.5176e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.8987e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.0956e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.7835e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.6968e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.8419e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.2441e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.6446e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4094e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.6698e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.7329e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.9151e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.5588e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.2092e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 1.2218e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.6104e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.2401e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.3221e-04 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 8.2282e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1305e-04 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.0014e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 1.0942e-04 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 8.2702e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1259e-04 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.9101e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 8.2071e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 8.2228e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.8527e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.3760e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 7.2426e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 6.7352e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 8.9702e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.5346e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 5.6812e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.7552e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.8082e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 4.0517e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.7660e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.0981e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.8215e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.1399e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.3056e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 3.1437e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.6462e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5300e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.9097e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.1701e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.9586e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0270 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.9070e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.9380e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.8063e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.6850e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.3526e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.5896e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.1292e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.0391e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.0656e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.0395e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 7.7023e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 9.8018e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.8881e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 9.2984e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.0776e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 9.8375e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 9.3675e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.4602e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 8.0510e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.2561e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.2432e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.6633e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.2243e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4405e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5299e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.0039e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 1.9648e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.6253e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6459e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.3462e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.5808e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.4443e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.5683e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.3434e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.3025e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1718e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.1414e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.1529e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.0691e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.3067e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.0833e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.1189e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.0006e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.0111e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0872e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 9.6835e-06 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.5804e-06 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0970e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 8.4238e-06 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0214 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 4.8191e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.1651e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.6254e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 3.9272e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.6606e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 3.2393e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9766e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.2111e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5621e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6855e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.6013e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.6355e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.2262e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4739e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7362e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.2173e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.7745e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4752e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.0954e-04 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4051e-04 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.1276e-04 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.6925e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.3286e-04 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 7.9273e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.6406e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0811e-04 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.0859e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 7.7610e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.8651e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.0857e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 7.1194e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 4.8350e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.9808e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.8851e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.0749e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.5032e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.1011e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.9976e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.2848e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.7288e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.4688e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.7187e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.0433e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7297e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.3215e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3696e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9469e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.1535e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4835e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0255 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 3.5421e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.1425e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.9222e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.8298e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.4269e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.4877e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.8187e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.7694e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.0364e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2947e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.5380e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.3582e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.2409e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2671e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 9.9839e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.1673e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.1626e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4133e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.2581e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 8.6455e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 8.6778e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 8.9381e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 8.1025e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.1596e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.1539e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.1977e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 5.1804e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.6290e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.2869e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.7631e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.6236e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2501e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 2.3802e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.4112e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.1590e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0657e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.8893e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.9334e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.8528e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.7540e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9500e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.7311e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.7605e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.7491e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.6134e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.6501e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.6650e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.6146e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.6036e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0194 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.6952e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.3048e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1918e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3722e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.3225e-05 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.0070e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 9.2440e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0054e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 8.1649e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.4751e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 8.8328e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.6473e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.0190e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.9946e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.3701e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.1002e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.1777e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 6.9287e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 6.4581e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 8.3769e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.2990e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.7889e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.8151e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 5.0520e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 5.7168e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.1239e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.6638e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.6132e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 4.2124e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.5073e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.2113e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.6111e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.9188e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.6733e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.5992e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.2052e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.2955e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.6228e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.8851e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.5896e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.8913e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.5851e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0074e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.6449e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.5955e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.6681e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.2121e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.2812e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2661e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 972us/step - loss: 0.0226 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.1546e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.8891e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 2.3411e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.4610e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.1408e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.7156e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.4269e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.4200e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.5343e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0787e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0135e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.5529e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.0979e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.4237e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.6201e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.5187e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 6.7524e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.2446e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.2502e-04 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 8.0033e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.6600e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 8.6506e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 8.0960e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 6.7777e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 7.8618e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.7526e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.3783e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 3.9341e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.7341e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.9743e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.2970e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.7204e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.0029e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 2.2607e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.4020e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.3473e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.8581e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.9017e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2952e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4660e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7725e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8376e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 1.7126e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.4942e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.6534e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.5359e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.8109e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.6919e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.5800e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0242 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 5.2047e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.0806e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.7829e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.2867e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.5043e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.0222e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 2.0804e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.2327e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 2.7074e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.3604e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9699e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.5929e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.3004e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.5783e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.1195e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0930e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0706e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.1740e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 8.0516e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 8.9775e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.1092e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 8.9505e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 5.1626e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.1421e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.0828e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 3.3040e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.2393e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.3741e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.9025e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.8383e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 3.8857e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6577e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.0219e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.6566e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.8566e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.7824e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6196e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.6665e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.1446e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.7786e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.1564e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5695e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0461e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.5490e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.6057e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.2976e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.5587e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.4444e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.0391e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 978us/step - loss: 0.0240 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.6151e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 2.2104e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1916e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.1867e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.4505e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 9.4331e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 8.9954e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.2331e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.6867e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.1412e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.2125e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 8.3696e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.0308e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.0034e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 7.6111e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 7.2226e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.0900e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 7.1367e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.9839e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 9.0198e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.1152e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.3372e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 6.2904e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 5.2145e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.5984e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.8438e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.6226e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.1734e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.1781e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.0834e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.1145e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.9466e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 2.5461e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 2.5345e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.3156e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.8070e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.2459e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.1893e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.4946e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.4786e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.5918e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.9494e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.6252e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2833e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.4728e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.4140e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3532e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.5111e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.1930e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0318 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 5.3081e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.7802e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.8883e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.4626e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7235e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9442e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.1716e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7714e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 1.2763e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 9.5934e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1059e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 8.9282e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 6.8511e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 6.2542e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.5535e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.8377e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.8449e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 6.5715e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.2779e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.3160e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 4.4619e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.5495e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.9873e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.6329e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.8669e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.1101e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.6616e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2321e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.0489e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.6236e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.8580e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9215e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.9611e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.0485e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.5853e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9592e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.4241e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.0658e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 1.9177e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 2.8335e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.3140e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.3399e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.6581e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.6165e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.7551e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.6112e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.9962e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.7609e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7882e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 961us/step - loss: 0.0236 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.9127e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 3.6525e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.8166e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.2766e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.2784e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 2.5587e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.0616e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.4389e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.6858e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.6484e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1012e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 9.2481e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.0444e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.7634e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.3412e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.3484e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.0814e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 3.7195e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.7133e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.4665e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.8579e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.0737e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.3677e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.8836e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.4224e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 3.1343e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.1429e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.4005e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.0079e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.2097e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.9813e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.6621e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.0154e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.9483e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6339e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9086e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.5607e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.0752e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3320e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4797e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.6033e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3276e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.3020e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 1.3091e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 1.1439e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.0101e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 1.1493e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.4332e-06 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 7.1880e-06 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0280 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.7029e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.4573e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.4352e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.0045e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0007e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.9336e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 8.0730e-05 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 6.2802e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 3.0490e-05 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0020e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.9687e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.1049e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.6620e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.0918e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.4419e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 4.0245e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.6521e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 6.9558e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.4782e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.5460e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.5170e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.2175e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 4.8608e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.7232e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.6137e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 4.0016e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.1214e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 4.6319e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 5.5854e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.1562e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 3.5736e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.2124e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 3.6449e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.0534e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.3249e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.4669e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.8210e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 2.4598e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.0663e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 2.4696e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.5709e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.7005e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.2393e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.1322e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7852e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.7019e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 1.7411e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.3004e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.5999e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 972us/step - loss: 0.0233 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 6.7536e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 6.8682e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 4.6241e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 5.1470e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.3790e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 4.4981e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.2753e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.4852e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.6441e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.9168e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.5755e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.2313e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.1984e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.2779e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.7305e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.1671e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.7966e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4720e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.6271e-04 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5152e-04 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.6316e-04 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.8407e-04 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4059e-04 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.2519e-04 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.2211e-04 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.2159e-04 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 1.1261e-04 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 8.5329e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.4924e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.3020e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 7.2853e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.7360e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 7.1519e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.0238e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 4.9037e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 5.5484e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 5.2220e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 3.6598e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.9047e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.0703e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.2269e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 5.1204e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.1331e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.8281e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.5947e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.8286e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 3.2218e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.2734e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 3.3642e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 955us/step - loss: 0.0217 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.6910e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.4071e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 3.0466e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 3.0256e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.2709e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.9558e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.4562e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.0349e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.4726e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.2143e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.0743e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1009e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.9190e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.1292e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 8.4251e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.7312e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0724e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.9671e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.7870e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.0953e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 9.3670e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 7.5036e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 9.9589e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 7.0912e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.9038e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.1080e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 7.0685e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.4004e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.8124e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 9.4375e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.8565e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.6049e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 6.9497e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.8886e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.2326e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.8529e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.3603e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.2643e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.8083e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.9491e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 3.4369e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.7461e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.8073e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 3.2332e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.2164e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.6708e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.3671e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.3107e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9040e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0304 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.0501e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4626e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.3896e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.1896e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 9.0337e-05 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 9.8916e-05 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.3475e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 9.5296e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.2607e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.2830e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.1344e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 849us/step - loss: 1.2104e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.0600e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 9.5860e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 8.4240e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.0745e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.0974e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 7.8138e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 8.9569e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 9.6084e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 8.3079e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 8.3577e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.3170e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 6.9904e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 6.7762e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step - loss: 6.9506e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 8.2828e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 6.3428e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 865us/step - loss: 4.9548e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.9600e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 4.8679e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.1074e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.2422e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.0725e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 4.1523e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 2.6722e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.5699e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.2748e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.7806e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 2.3782e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.0210e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 854us/step - loss: 1.8553e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9057e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.7690e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.7055e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.3326e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.6059e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.3394e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 860us/step - loss: 1.3521e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 961us/step - loss: 0.0238 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.9144e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 3.6113e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 2.9976e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 1.9791e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.4192e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.4719e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.7643e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 2.6835e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step - loss: 1.6397e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.5648e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.7137e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.9477e-04 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 1.3624e-04 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.7284e-04 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 1.7882e-04 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.1916e-04 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2845e-04 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 955us/step - loss: 1.0944e-04 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.1954e-04 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1924e-04 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.1275e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0732e-04 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.4160e-04 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.0491e-04 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.0968e-04 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 9.8210e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.4524e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 6.9503e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.1994e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 6.7299e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 6.6759e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 5.5018e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 7.3081e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.0730e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.8203e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 4.9415e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.4312e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 871us/step - loss: 4.6140e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.5987e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.9523e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 3.0246e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 3.2383e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 2.2697e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.7798e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.5769e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.3934e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 2.1086e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.3678e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.1210e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 983us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 1s 950us/step - loss: 0.0284 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 9.6166e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step - loss: 5.6977e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.0400e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.7663e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 3.0734e-04 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 2.9431e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9676e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.6445e-04 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.3912e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.1139e-04 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0957e-04 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 9.0413e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 6.4546e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 7.2428e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 5.9346e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 5.4477e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - loss: 4.8184e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.2043e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 4.8156e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 4.4272e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.0058e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 4.3927e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 4.2456e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.8948e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 978us/step - loss: 4.1329e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.4735e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.4948e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.3372e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 3.9323e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.1927e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 3.2231e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.2872e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 3.3882e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 922us/step - loss: 2.6526e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.1675e-05 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - loss: 3.3668e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.2573e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.1784e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.4249e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 2.7835e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 2.5556e-05 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 2.2790e-05 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 2.1547e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.5515e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 1.9167e-05 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.8769e-05 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.0320e-05 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7700e-05 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.9538e-05 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 938us/step
C:\Users\UsX\AppData\Local\Temp\ipykernel_22772\2509651175.py:19: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead. df.interpolate(method='linear', inplace=True)
Epoch 1/50
c:\Users\UsX\miniconda3\envs\nsa\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
179/179 ━━━━━━━━━━━━━━━━━━━━ 3s 1ms/step - loss: 0.0245 Epoch 2/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.0477e-04 Epoch 3/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 2.9595e-04 Epoch 4/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.5860e-04 Epoch 5/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 1.4406e-04 Epoch 6/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 9.0335e-05 Epoch 7/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 1.4537e-04 Epoch 8/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6644e-04 Epoch 9/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - loss: 9.9606e-05 Epoch 10/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 1.1602e-04 Epoch 11/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 6.8369e-05 Epoch 12/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 9.6520e-05 Epoch 13/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 6.5333e-05 Epoch 14/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step - loss: 5.5612e-05 Epoch 15/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 5.5655e-05 Epoch 16/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.5708e-05 Epoch 17/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 888us/step - loss: 4.7747e-05 Epoch 18/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 4.6945e-05 Epoch 19/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 3.7663e-05 Epoch 20/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 3.8075e-05 Epoch 21/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 3.5487e-05 Epoch 22/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 3.8553e-05 Epoch 23/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 2.5861e-05 Epoch 24/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 1.5228e-05 Epoch 25/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.6080e-05 Epoch 26/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.3325e-05 Epoch 27/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.7166e-05 Epoch 28/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.2500e-05 Epoch 29/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1312e-05 Epoch 30/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1513e-05 Epoch 31/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.0280e-05 Epoch 32/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.3059e-05 Epoch 33/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.2035e-05 Epoch 34/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 1.1734e-05 Epoch 35/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 910us/step - loss: 1.1382e-05 Epoch 36/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 967us/step - loss: 9.7618e-06 Epoch 37/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 905us/step - loss: 1.0593e-05 Epoch 38/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 899us/step - loss: 1.1497e-05 Epoch 39/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - loss: 1.0027e-05 Epoch 40/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 1.0446e-05 Epoch 41/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.2686e-05 Epoch 42/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 893us/step - loss: 7.8921e-06 Epoch 43/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 933us/step - loss: 7.6025e-06 Epoch 44/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 1.0150e-05 Epoch 45/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 1.2443e-05 Epoch 46/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 916us/step - loss: 8.8960e-06 Epoch 47/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 950us/step - loss: 9.1038e-06 Epoch 48/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 882us/step - loss: 9.8819e-06 Epoch 49/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - loss: 9.3785e-06 Epoch 50/50 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.6949e-06 179/179 ━━━━━━━━━━━━━━━━━━━━ 0s 972us/step